# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations

import contextlib
import warnings
from collections.abc import Mapping
from copy import deepcopy
from dataclasses import dataclass

import torch
from tensordict import (
    is_tensor_collection,
    TensorDict,
    TensorDictBase,
    TensorDictParams,
)
from tensordict.nn import (
    composite_lp_aggregate,
    CompositeDistribution,
    dispatch,
    ProbabilisticTensorDictModule,
    ProbabilisticTensorDictSequential,
    set_composite_lp_aggregate,
    TensorDictModule,
)
from tensordict.utils import NestedKey
from torch import distributions as d

from torchrl._utils import _standardize, logger as torchrl_logger, VERBOSE
from torchrl.objectives.common import LossModule
from torchrl.objectives.utils import (
    _cache_values,
    _clip_value_loss,
    _GAMMA_LMBDA_DEPREC_ERROR,
    _maybe_add_or_extend_key,
    _maybe_get_or_select,
    _reduce,
    _sum_td_features,
    default_value_kwargs,
    distance_loss,
    ValueEstimators,
)
from torchrl.objectives.value import (
    GAE,
    TD0Estimator,
    TD1Estimator,
    TDLambdaEstimator,
    VTrace,
)


class PPOLoss(LossModule):
    """A parent PPO loss class.

    PPO (Proximal Policy Optimization) is a model-free, online RL algorithm
    that makes use of a recorded (batch of)
    trajectories to perform several optimization steps, while actively
    preventing the updated policy to deviate too
    much from its original parameter configuration.

    PPO loss can be found in different flavors, depending on the way the
    constrained optimization is implemented: ClipPPOLoss and KLPENPPOLoss.
    Unlike its subclasses, this class does not implement any regularization
    and should therefore be used cautiously.

    For more details regarding PPO, refer to: "Proximal Policy Optimization Algorithms",
    https://arxiv.org/abs/1707.06347

    Args:
        actor_network (ProbabilisticTensorDictSequential): policy operator.
            Typically, a :class:`~tensordict.nn.ProbabilisticTensorDictSequential` subclass taking observations
            as input and outputting an action (or actions) as well as its log-probability value.
        critic_network (ValueOperator): value operator. The critic will usually take the observations as input
            and return a scalar value (``state_value`` by default) in the output keys.

    .. note::
        While this loss module does not enforce any specific model mode (train/eval), it is highly recommended
        to keep your model in eval mode during RL training to ensure deterministic behavior.
        A failure to learn due to a train/eval mode mismatch is often observed when the Effective Sample Size (ESS)
        drops or increases significantly (see note below).

    .. note::
        The PPO loss exposes a couple of additional metrics that can be used to monitor the training process:

        - The clip fraction is the ratio of the number of clipped weights in the PPO loss (i.e. the ratio of the number of weights that were clipped to the total number of weights).
        - The Effective Sample Size (ESS) is a measure of the effective number of samples in the batch, computed as the inverse of the sum of the squared importance weights.
          A value of 1 indicates that the importance weights are all equal to 1 (i.e., the samples are equally weighted).
          Any value below 1 indicates that the samples are not equally weighted, and the ESS is a measure of the effective number of samples.
          If the value drops or increases significantly, it often indicates issues with the model configuration (such as a train/eval mode mismatch, or a large policy update).

    Keyword Args:
        entropy_bonus (bool, optional): if ``True``, an entropy bonus will be added to the
            loss to favour exploratory policies.
        samples_mc_entropy (int, optional): if the distribution retrieved from the policy
            operator does not have a closed form
            formula for the entropy, a Monte-Carlo estimate will be used.
            ``samples_mc_entropy`` will control how many
            samples will be used to compute this estimate.
            Defaults to ``1``.
        entropy_coeff: scalar | Mapping[NestedKey, scalar], optional): entropy multiplier when computing the total loss.
            * **Scalar**: one value applied to the summed entropy of every action head.
            * **Mapping** ``{head_name: coeff}`` gives an individual coefficient for each action-head's entropy.
            Defaults to ``0.01``.

            See :ref:`ppo_entropy_coefficients` for detailed usage examples and troubleshooting.
        log_explained_variance (bool, optional): if ``True``, the explained variance of the critic
            predictions w.r.t. value targets will be computed and logged as ``"explained_variance"``.
            This can help monitor critic quality during training. Best possible score is 1.0, lower values are worse. Defaults to ``True``.
        critic_coeff (scalar, optional): critic loss multiplier when computing the total
            loss. Defaults to ``1.0``. Set ``critic_coeff`` to ``None`` to exclude the value
            loss from the forward outputs.
        loss_critic_type (str, optional): loss function for the value discrepancy.
            Can be one of "l1", "l2" or "smooth_l1". Defaults to ``"smooth_l1"``.
        normalize_advantage (bool, optional): if ``True``, the advantage will be normalized
            before being used. Defaults to ``False``.
        normalize_advantage_exclude_dims (Tuple[int], optional): dimensions to exclude from the advantage
            standardization. Negative dimensions are valid. This is useful in multiagent (or multiobjective) settings
            where the agent (or objective) dimension may be excluded from the reductions. Default: ().
        separate_losses (bool, optional): if ``True``, shared parameters between
            policy and critic will only be trained on the policy loss.
            Defaults to ``False``, i.e., gradients are propagated to shared
            parameters for both policy and critic losses.
        advantage_key (str, optional): [Deprecated, use set_keys(advantage_key=advantage_key) instead]
            The input tensordict key where the advantage is
            expected to be written. Defaults to ``"advantage"``.
        value_target_key (str, optional): [Deprecated, use set_keys(value_target_key=value_target_key) instead]
            The input tensordict key where the target state
            value is expected to be written. Defaults to ``"value_target"``.
        value_key (str, optional): [Deprecated, use set_keys(value_key) instead]
            The input tensordict key where the state
            value is expected to be written. Defaults to ``"state_value"``.
        functional (bool, optional): whether modules should be functionalized.
            Functionalizing permits features like meta-RL, but makes it
            impossible to use distributed models (DDP, FSDP, ...) and comes
            with a little cost. Defaults to ``True``.
        reduction (str, optional): Specifies the reduction to apply to the output:
            ``"none"`` | ``"mean"`` | ``"sum"``. ``"none"``: no reduction will be applied,
            ``"mean"``: the sum of the output will be divided by the number of
            elements in the output, ``"sum"``: the output will be summed. Default: ``"mean"``.
        clip_value (:obj:`float`, optional): If provided, it will be used to compute a clipped version of the value
            prediction with respect to the input tensordict value estimate and use it to calculate the value loss.
            The purpose of clipping is to limit the impact of extreme value predictions, helping stabilize training
            and preventing large updates. However, it will have no impact if the value estimate was done by the current
            version of the value estimator. Defaults to ``None``.
        device (torch.device, optional): device of the buffers. Defaults to ``None``.

            .. note:: Parameters and buffers from the policy / critic will not be cast to that device to ensure that
                the storages match the ones that are passed to other components, such as data collectors.

    .. note::
      The advantage (typically GAE) can be computed by the loss function or
      in the training loop. The latter option is usually preferred, but this is
      up to the user to choose which option is to be preferred.
      If the advantage key (``"advantage`` by default) is not present in the
      input tensordict, the advantage will be computed by the :meth:`~.forward`
      method.

        >>> ppo_loss = PPOLoss(actor, critic)
        >>> advantage = GAE(critic)
        >>> data = next(datacollector)
        >>> losses = ppo_loss(data)
        >>> # equivalent
        >>> advantage(data)
        >>> losses = ppo_loss(data)

      A custom advantage module can be built using :meth:`~.make_value_estimator`.
      The default is :class:`~torchrl.objectives.value.GAE` with hyperparameters
      dictated by :func:`~torchrl.objectives.utils.default_value_kwargs`.

        >>> ppo_loss = PPOLoss(actor, critic)
        >>> ppo_loss.make_value_estimator(ValueEstimators.TDLambda)
        >>> data = next(datacollector)
        >>> losses = ppo_loss(data)

    .. note::
      If the actor and the value function share parameters, one can avoid
      calling the common module multiple times by passing only the head of the
      value network to the PPO loss module:

        >>> common = SomeModule(in_keys=["observation"], out_keys=["hidden"])
        >>> actor_head = SomeActor(in_keys=["hidden"])
        >>> value_head = SomeValue(in_keys=["hidden"])
        >>> # first option, with 2 calls on the common module
        >>> model = ActorValueOperator(common, actor_head, value_head)
        >>> loss_module = PPOLoss(model.get_policy_operator(), model.get_value_operator())
        >>> # second option, with a single call to the common module
        >>> loss_module = PPOLoss(ProbabilisticTensorDictSequential(model, actor_head), value_head)

      This will work regardless of whether separate_losses is activated or not.

    Examples:
        >>> import torch
        >>> from torch import nn
        >>> from torchrl.data.tensor_specs import Bounded
        >>> from torchrl.modules.distributions import NormalParamExtractor, TanhNormal
        >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator
        >>> from torchrl.modules.tensordict_module.common import SafeModule
        >>> from torchrl.objectives.ppo import PPOLoss
        >>> from tensordict import TensorDict
        >>> n_act, n_obs = 4, 3
        >>> spec = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,))
        >>> base_layer = nn.Linear(n_obs, 5)
        >>> net = nn.Sequential(base_layer, nn.Linear(5, 2 * n_act), NormalParamExtractor())
        >>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
        >>> actor = ProbabilisticActor(
        ...     module=module,
        ...     distribution_class=TanhNormal,
        ...     in_keys=["loc", "scale"],
        ...     spec=spec)
        >>> module = nn.Sequential(base_layer, nn.Linear(5, 1))
        >>> value = ValueOperator(
        ...     module=module,
        ...     in_keys=["observation"])
        >>> loss = PPOLoss(actor, value)
        >>> batch = [2, ]
        >>> action = spec.rand(batch)
        >>> data = TensorDict({"observation": torch.randn(*batch, n_obs),
        ...         "action": action,
        ...         "action_log_prob": torch.randn_like(action[..., 1]),
        ...         ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool),
        ...         ("next", "terminated"): torch.zeros(*batch, 1, dtype=torch.bool),
        ...         ("next", "reward"): torch.randn(*batch, 1),
        ...         ("next", "observation"): torch.randn(*batch, n_obs),
        ...     }, batch)
        >>> loss(data)
        TensorDict(
            fields={
                entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                explained_variance: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),
                kl_approx: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                loss_critic: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                loss_entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
                loss_objective: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([]),
            device=None,
            is_shared=False)

    This class is compatible with non-tensordict based modules too and can be
    used without recurring to any tensordict-related primitive. In this case,
    the expected keyword arguments are:
    ``["action", "sample_log_prob", "next_reward", "next_done", "next_terminated"]`` + in_keys of the actor and value network.
    The return value is a tuple of tensors in the following order:
    ``["loss_objective"]`` + ``["entropy", "loss_entropy"]`` if entropy_bonus is set + ``"loss_critic"`` if critic_coeff is not ``None``.
    The output keys can also be filtered using :meth:`PPOLoss.select_out_keys` method.

    Examples:
        >>> import torch
        >>> from torch import nn
        >>> from torchrl.data.tensor_specs import Bounded
        >>> from torchrl.modules.distributions import NormalParamExtractor, TanhNormal
        >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator
        >>> from torchrl.modules.tensordict_module.common import SafeModule
        >>> from torchrl.objectives.ppo import PPOLoss
        >>> n_act, n_obs = 4, 3
        >>> spec = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,))
        >>> base_layer = nn.Linear(n_obs, 5)
        >>> net = nn.Sequential(base_layer, nn.Linear(5, 2 * n_act), NormalParamExtractor())
        >>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
        >>> actor = ProbabilisticActor(
        ...     module=module,
        ...     distribution_class=TanhNormal,
        ...     in_keys=["loc", "scale"],
        ...     spec=spec)
        >>> module = nn.Sequential(base_layer, nn.Linear(5, 1))
        >>> value = ValueOperator(
        ...     module=module,
        ...     in_keys=["observation"])
        >>> loss = PPOLoss(actor, value)
        >>> loss.set_keys(sample_log_prob="sampleLogProb")
        >>> _ = loss.select_out_keys("loss_objective")
        >>> batch = [2, ]
        >>> action = spec.rand(batch)
        >>> loss_objective = loss(
        ...         observation=torch.randn(*batch, n_obs),
        ...         action=action,
        ...         sampleLogProb=torch.randn_like(action[..., 1]) / 10,
        ...         next_done=torch.zeros(*batch, 1, dtype=torch.bool),
        ...         next_terminated=torch.zeros(*batch, 1, dtype=torch.bool),
        ...         next_reward=torch.randn(*batch, 1),
        ...         next_observation=torch.randn(*batch, n_obs))
        >>> loss_objective.backward()

    **Simple Entropy Coefficient Examples**:
        >>> # Scalar entropy coefficient (default behavior)
        >>> loss = PPOLoss(actor, critic, entropy_coeff=0.01)
        >>>
        >>> # Per-head entropy coefficients (for composite action spaces)
        >>> entropy_coeff = {
        ...     ("agent0", "action_log_prob"): 0.01,  # Low exploration
        ...     ("agent1", "action_log_prob"): 0.05,  # High exploration
        ... }
        >>> loss = PPOLoss(actor, critic, entropy_coeff=entropy_coeff)

    .. note::
      There is an exception regarding compatibility with non-tensordict-based modules.
      If the actor network is probabilistic and uses a :class:`~tensordict.nn.distributions.CompositeDistribution`,
      this class must be used with tensordicts and cannot function as a tensordict-independent module.
      This is because composite action spaces inherently rely on the structured representation of data provided by
      tensordicts to handle their actions.

    .. _ppo_entropy_coefficients:

    .. note::
        **Entropy Bonus and Coefficient Management**

        The entropy bonus encourages exploration by adding the negative entropy of the policy to the loss.
        This can be configured in two ways:

        **Scalar Coefficient (Default)**: Use a single coefficient for all action heads:
            >>> loss = PPOLoss(actor, critic, entropy_coeff=0.01)

        **Per-Head Coefficients**: Use different coefficients for different action components:
            >>> # For a robot with movement and gripper actions
            >>> entropy_coeff = {
            ...     ("agent0", "action_log_prob"): 0.01,  # Movement: low exploration
            ...     ("agent1", "action_log_prob"): 0.05,  # Gripper: high exploration
            ... }
            >>> loss = PPOLoss(actor, critic, entropy_coeff=entropy_coeff)

        **Key Requirements**: When using per-head coefficients, you must provide the full nested key
        path to each action head's log probability (e.g., `("agent0", "action_log_prob")`).

        **Monitoring Entropy Loss**:

        When using composite action spaces, the loss output includes:
        - `"entropy"`: Summed entropy across all action heads (for logging)
        - `"composite_entropy"`: Individual entropy values for each action head
        - `"loss_entropy"`: The weighted entropy loss term

        Example output:
            >>> result = loss(data)
            >>> print(result["entropy"])           # Total entropy: 2.34
            >>> print(result["composite_entropy"]) # Per-head: {"movement": 1.2, "gripper": 1.14}
            >>> print(result["loss_entropy"])      # Weighted loss: -0.0234

        **Common Issues**:

        **KeyError: "Missing entropy coeff for head 'head_name'"**:
            - Ensure you provide coefficients for ALL action heads
            - Use full nested keys: `("head_name", "action_log_prob")`
            - Check that your action space structure matches the coefficient mapping

        **Incorrect Entropy Calculation**:
            - Call `set_composite_lp_aggregate(False).set()` before creating your policy
            - Verify that your action space uses :class:`~tensordict.nn.distributions.CompositeDistribution`
    """

    @dataclass
    class _AcceptedKeys:
        """Maintains default values for all configurable tensordict keys.

        This class defines which tensordict keys can be set using '.set_keys(key_name=key_value)' and their
        default values

        Attributes:
            advantage (NestedKey): The input tensordict key where the advantage is expected.
                Will be used for the underlying value estimator. Defaults to ``"advantage"``.
            value_target (NestedKey): The input tensordict key where the target state value is expected.
                Will be used for the underlying value estimator Defaults to ``"value_target"``.
            value (NestedKey): The input tensordict key where the state value is expected.
                Will be used for the underlying value estimator. Defaults to ``"state_value"``.
            sample_log_prob (NestedKey or list of nested keys): The input tensordict key where the
               sample log probability is expected.
               Defaults to ``"sample_log_prob"`` when :func:`~tensordict.nn.composite_lp_aggregate` returns `True`,
                `"action_log_prob"`  otherwise.
            action (NestedKey or list of nested keys): The input tensordict key where the action is expected.
                Defaults to ``"action"``.
            reward (NestedKey or list of nested keys): The input tensordict key where the reward is expected.
                Will be used for the underlying value estimator. Defaults to ``"reward"``.
            done (NestedKey or list of nested keys): The key in the input TensorDict that indicates
                whether a trajectory is done. Will be used for the underlying value estimator.
                Defaults to ``"done"``.
            terminated (NestedKey or list of nested keys): The key in the input TensorDict that indicates
                whether a trajectory is terminated. Will be used for the underlying value estimator.
                Defaults to ``"terminated"``.
        """

        advantage: NestedKey = "advantage"
        value_target: NestedKey = "value_target"
        value: NestedKey = "state_value"
        sample_log_prob: NestedKey | list[NestedKey] | None = None
        action: NestedKey | list[NestedKey] = "action"
        reward: NestedKey | list[NestedKey] = "reward"
        done: NestedKey | list[NestedKey] = "done"
        terminated: NestedKey | list[NestedKey] = "terminated"

        def __post_init__(self):
            if self.sample_log_prob is None:
                if composite_lp_aggregate(nowarn=True):
                    self.sample_log_prob = "sample_log_prob"
                else:
                    self.sample_log_prob = "action_log_prob"

    default_keys = _AcceptedKeys
    tensor_keys: _AcceptedKeys
    default_value_estimator = ValueEstimators.GAE

    actor_network: ProbabilisticTensorDictModule
    critic_network: TensorDictModule
    actor_network_params: TensorDictParams
    critic_network_params: TensorDictParams
    target_actor_network_params: TensorDictParams
    target_critic_network_params: TensorDictParams

    def __init__(
        self,
        actor_network: ProbabilisticTensorDictSequential | None = None,
        critic_network: TensorDictModule | None = None,
        *,
        entropy_bonus: bool = True,
        samples_mc_entropy: int = 1,
        entropy_coeff: float | Mapping[NestedKey, float] | None = None,
        log_explained_variance: bool = True,
        critic_coeff: float | None = None,
        loss_critic_type: str = "smooth_l1",
        normalize_advantage: bool = False,
        normalize_advantage_exclude_dims: tuple[int] = (),
        gamma: float | None = None,
        separate_losses: bool = False,
        advantage_key: str | None = None,
        value_target_key: str | None = None,
        value_key: str | None = None,
        functional: bool = True,
        actor: ProbabilisticTensorDictSequential = None,
        critic: ProbabilisticTensorDictSequential = None,
        reduction: str | None = None,
        clip_value: float | None = None,
        device: torch.device | None = None,
        **kwargs,
    ):
        if actor is not None:
            actor_network = actor
            del actor
        if critic is not None:
            critic_network = critic
            del critic

        # Handle deprecated critic_coef argument
        if "critic_coef" in kwargs:
            if critic_coeff is not None:
                raise ValueError("Cannot specify both 'critic_coef' and 'critic_coeff'")
            warnings.warn(
                "'critic_coef' is deprecated and will be removed in torchrl v0.11. Please use 'critic_coeff' instead.",
                DeprecationWarning,
            )
            critic_coeff = kwargs.pop("critic_coef")

        if critic_coeff is None and critic_network is not None:
            critic_coeff = 1.0
        elif critic_coeff in (None, 0) and critic_network is not None:
            critic_coeff = None

        if actor_network is None or (
            critic_network is None and critic_coeff not in (None, 0.0)
        ):
            raise TypeError(
                "Missing positional arguments actor_network or critic_network."
            )
        if reduction is None:
            reduction = "mean"

        self._functional = functional
        self._in_keys = None
        self._out_keys = None
        super().__init__()
        if functional:
            self.convert_to_functional(actor_network, "actor_network")
        else:
            self.actor_network = actor_network
            self.actor_network_params = None
            self.target_actor_network_params = None

        if separate_losses:
            # we want to make sure there are no duplicates in the params: the
            # params of critic must be refs to actor if they're shared
            policy_params = list(actor_network.parameters())
        else:
            policy_params = None
        if functional and critic_network is not None:
            self.convert_to_functional(
                critic_network, "critic_network", compare_against=policy_params
            )
        else:
            self.critic_network = critic_network
            self.critic_network_params = None
            self.target_critic_network_params = None

        self.log_explained_variance = log_explained_variance
        self.samples_mc_entropy = samples_mc_entropy
        self.entropy_bonus = entropy_bonus
        self.separate_losses = separate_losses
        self.reduction = reduction

        if device is None:
            try:
                device = next(self.parameters()).device
            except (AttributeError, StopIteration):
                device = getattr(
                    torch, "get_default_device", lambda: torch.device("cpu")
                )()

        # Handle deprecated entropy_coef argument
        if "entropy_coef" in kwargs:
            if entropy_coeff is not None:  # Check if entropy_coeff was explicitly set
                raise ValueError(
                    "Cannot specify both 'entropy_coef' and 'entropy_coeff'"
                )
            warnings.warn(
                "'entropy_coef' is deprecated and will be removed in torchrl v0.11. Please use 'entropy_coeff' instead.",
                DeprecationWarning,
            )
            entropy_coeff = kwargs.pop("entropy_coef")

        # Set default value if None
        if entropy_coeff is None:
            entropy_coeff = 0.01

        if isinstance(entropy_coeff, Mapping):
            # Store the mapping for per-head coefficients
            self._entropy_coeff_map = {k: float(v) for k, v in entropy_coeff.items()}
            # Register an empty buffer for compatibility
            self.register_buffer("entropy_coeff", torch.tensor(0.0))
        elif isinstance(entropy_coeff, (float, int, torch.Tensor)):
            # Register the scalar entropy coefficient
            coeff = (
                float(entropy_coeff)
                if not torch.is_tensor(entropy_coeff)
                else float(entropy_coeff.item())
            )
            self.register_buffer("entropy_coeff", torch.tensor(coeff))
            self._entropy_coeff_map = None
        else:
            raise TypeError("entropy_coeff must be a float or a Mapping[str, float]")
        if critic_coeff is not None:
            self.register_buffer(
                "critic_coeff", torch.tensor(critic_coeff, device=device)
            )
        else:
            self.critic_coeff = None
        self._has_critic = bool(self.critic_coeff is not None and self.critic_coeff > 0)
        self.loss_critic_type = loss_critic_type
        self.normalize_advantage = normalize_advantage
        self.normalize_advantage_exclude_dims = normalize_advantage_exclude_dims

        if gamma is not None:
            raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR)
        self._set_deprecated_ctor_keys(
            advantage=advantage_key,
            value_target=value_target_key,
            value=value_key,
        )

        if clip_value is not None:
            if isinstance(clip_value, float):
                clip_value = torch.tensor(clip_value, device=device)
            elif isinstance(clip_value, torch.Tensor):
                if clip_value.numel() != 1:
                    raise ValueError(
                        f"clip_value must be a float or a scalar tensor, got {clip_value}."
                    )
            else:
                raise ValueError(
                    f"clip_value must be a float or a scalar tensor, got {clip_value}."
                )
            self.register_buffer("clip_value", clip_value.to(device))
        else:
            self.clip_value = None
        try:
            log_prob_keys = self.actor_network.log_prob_keys
            action_keys = self.actor_network.dist_sample_keys
            if len(log_prob_keys) > 1:
                self.set_keys(sample_log_prob=log_prob_keys, action=action_keys)
            else:
                self.set_keys(sample_log_prob=log_prob_keys[0], action=action_keys[0])
        except AttributeError:
            pass

    @property
    def functional(self):
        return self._functional

    def _set_in_keys(self):
        keys = []
        _maybe_add_or_extend_key(keys, self.actor_network.in_keys)
        _maybe_add_or_extend_key(keys, self.actor_network.in_keys, "next")
        if self.critic_network is not None:
            _maybe_add_or_extend_key(keys, self.critic_network.in_keys)
        _maybe_add_or_extend_key(keys, self.tensor_keys.action)
        _maybe_add_or_extend_key(keys, self.tensor_keys.sample_log_prob)
        _maybe_add_or_extend_key(keys, self.tensor_keys.reward, "next")
        _maybe_add_or_extend_key(keys, self.tensor_keys.done, "next")
        _maybe_add_or_extend_key(keys, self.tensor_keys.terminated, "next")

        self._in_keys = list(set(keys))

    @property
    def in_keys(self):
        if self._in_keys is None:
            self._set_in_keys()
        return self._in_keys

    @in_keys.setter
    def in_keys(self, values):
        self._in_keys = values

    @property
    def out_keys(self):
        if self._out_keys is None:
            keys = ["loss_objective"]
            if self.entropy_bonus:
                keys.extend(["entropy", "loss_entropy"])
            if self.loss_critic:
                keys.append("loss_critic")
            if self.clip_value:
                keys.append("value_clip_fraction")
            self._out_keys = keys
        return self._out_keys

    @out_keys.setter
    def out_keys(self, values):
        self._out_keys = values

    def _forward_value_estimator_keys(self, **kwargs) -> None:
        if hasattr(self, "_value_estimator") and self._value_estimator is not None:
            self._value_estimator.set_keys(
                advantage=self.tensor_keys.advantage,
                value_target=self.tensor_keys.value_target,
                value=self.tensor_keys.value,
                reward=self.tensor_keys.reward,
                done=self.tensor_keys.done,
                terminated=self.tensor_keys.terminated,
                sample_log_prob=self.tensor_keys.sample_log_prob,
            )
        self._set_in_keys()

    def reset(self) -> None:
        pass

    def _get_entropy(
        self, dist: d.Distribution, adv_shape: torch.Size
    ) -> torch.Tensor | TensorDict:
        try:
            entropy = dist.entropy()
            if not entropy.isfinite().all():
                del entropy
                if VERBOSE:
                    torchrl_logger.info(
                        "Entropy is not finite. Using Monte Carlo sampling."
                    )
                raise NotImplementedError
        except NotImplementedError:
            if VERBOSE:
                torchrl_logger.warning(
                    f"Entropy not implemented for {type(dist)} or is not finite. Using Monte Carlo sampling."
                )
            if getattr(dist, "has_rsample", False):
                x = dist.rsample((self.samples_mc_entropy,))
            else:
                x = dist.sample((self.samples_mc_entropy,))
            with set_composite_lp_aggregate(False) if isinstance(
                dist, CompositeDistribution
            ) else contextlib.nullcontext():
                log_prob = dist.log_prob(x)
                if is_tensor_collection(log_prob):
                    if isinstance(self.tensor_keys.sample_log_prob, NestedKey):
                        log_prob = log_prob.get(self.tensor_keys.sample_log_prob)
                    else:
                        log_prob = log_prob.select(*self.tensor_keys.sample_log_prob)

            entropy = -log_prob.mean(0)
            if is_tensor_collection(entropy) and entropy.batch_size != adv_shape:
                entropy.batch_size = adv_shape
        return entropy.unsqueeze(-1)

    def _get_cur_log_prob(self, tensordict):
        if isinstance(
            self.actor_network,
            (ProbabilisticTensorDictSequential, ProbabilisticTensorDictModule),
        ) or hasattr(self.actor_network, "get_dist"):
            # assert tensordict['log_probs'].requires_grad
            # assert tensordict['logits'].requires_grad
            with self.actor_network_params.to_module(
                self.actor_network
            ) if self.functional else contextlib.nullcontext():
                dist = self.actor_network.get_dist(tensordict)
            is_composite = isinstance(dist, CompositeDistribution)

            if is_composite:
                action = tensordict.select(
                    *(
                        (self.tensor_keys.action,)
                        if isinstance(self.tensor_keys.action, NestedKey)
                        else self.tensor_keys.action
                    )
                )
            else:
                action = _maybe_get_or_select(tensordict, self.tensor_keys.action)

            if action.requires_grad:
                raise RuntimeError(
                    f"tensordict stored {self.tensor_keys.action} requires grad."
                )
            log_prob = dist.log_prob(action)
        else:
            raise NotImplementedError(
                "Only probabilistic modules from tensordict.nn are currently supported. "
                "If you need to implement a custom logic to retrieve the log-probs (to compute "
                "the PPO objective) or the distribution (for the PPO entropy), please augment "
                f"the {type(self).__class__} by implementing your own logic in _get_cur_log_prob."
            )
            # with self.actor_network_params.to_module(
            #     self.actor_network
            # ) if self.functional else contextlib.nullcontext():
            #     td = self.actor_network(tensordict)
            #     log_prob = td.get(self.tensor_keys.sample_log_prob)
            #     dist = torch.distributions.Categorical(td.get("logits"))
            #     is_composite = False
        return log_prob, dist, is_composite

    def _log_weight(
        self, tensordict: TensorDictBase, adv_shape: torch.Size
    ) -> tuple[torch.Tensor, d.Distribution, torch.Tensor]:
        prev_log_prob = _maybe_get_or_select(
            tensordict,
            self.tensor_keys.sample_log_prob,
            adv_shape,
        )
        if prev_log_prob is None:
            raise KeyError(
                f"Couldn't find the log-prob {self.tensor_keys.sample_log_prob} in the input data."
            )
        if prev_log_prob.requires_grad:
            raise RuntimeError(
                f"tensordict stored {self.tensor_keys.sample_log_prob} requires grad."
            )

        log_prob, dist, is_composite = self._get_cur_log_prob(tensordict)

        if is_composite:
            with set_composite_lp_aggregate(False):
                if not is_tensor_collection(prev_log_prob):
                    # this isn't great: in general, multi-head actions should have a composite log-prob too
                    warnings.warn(
                        "You are using a composite distribution, yet your log-probability is a tensor. "
                        "Make sure you have called tensordict.nn.set_composite_lp_aggregate(False).set() at "
                        "the beginning of your script to get a proper composite log-prob.",
                        category=UserWarning,
                    )

                    if is_tensor_collection(log_prob):
                        log_prob = _sum_td_features(log_prob)
                        log_prob.view_as(prev_log_prob)
                if log_prob.batch_size != adv_shape:
                    log_prob.batch_size = adv_shape
        log_weight = (log_prob - prev_log_prob).unsqueeze(-1)
        if is_tensor_collection(log_weight):
            log_weight = _sum_td_features(log_weight)
            log_weight = log_weight.view(adv_shape).unsqueeze(-1)

        kl_approx = (prev_log_prob - log_prob).unsqueeze(-1)
        if is_tensor_collection(kl_approx):
            kl_approx = _sum_td_features(kl_approx)

        return log_weight, dist, kl_approx

    def loss_critic(
        self, tensordict: TensorDictBase
    ) -> tuple[torch.Tensor | TensorDict, ...]:
        """Returns the critic loss multiplied by ``critic_coeff``, if it is not ``None``."""
        # TODO: if the advantage is gathered by forward, this introduces an
        # overhead that we could easily reduce.
        if self.separate_losses:
            tensordict = tensordict.detach()
        target_return = tensordict.get(
            self.tensor_keys.value_target, None
        )  # TODO: None soon to be removed
        if target_return is None:
            raise KeyError(
                f"the key {self.tensor_keys.value_target} was not found in the input tensordict. "
                f"Make sure you provided the right key and the value_target (i.e. the target "
                f"return) has been retrieved accordingly. Advantage classes such as GAE, "
                f"TDLambdaEstimate and TDEstimate all return a 'value_target' entry that "
                f"can be used for the value loss."
            )

        if self.clip_value:
            old_state_value = tensordict.get(self.tensor_keys.value)
            if old_state_value is None:
                raise KeyError(
                    f"clip_value is set to {self.clip_value}, but "
                    f"the key {self.tensor_keys.value} was not found in the input tensordict. "
                    f"Make sure that the 'value_key' passed to PPO exists in the input tensordict."
                )

        with self.critic_network_params.to_module(
            self.critic_network
        ) if self.functional else contextlib.nullcontext():
            state_value_td = self.critic_network(tensordict)

        state_value = state_value_td.get(self.tensor_keys.value)
        if state_value is None:
            raise KeyError(
                f"the key {self.tensor_keys.value} was not found in the critic output tensordict. "
                f"Make sure that the 'value_key' passed to PPO is accurate."
            )

        loss_value = distance_loss(
            target_return,
            state_value,
            loss_function=self.loss_critic_type,
        )

        clip_fraction = None
        if self.clip_value:
            loss_value, clip_fraction = _clip_value_loss(
                old_state_value,
                state_value,
                self.clip_value,
                target_return,
                loss_value,
                self.loss_critic_type,
            )

        explained_variance = None
        if self.log_explained_variance:
            with torch.no_grad():  # <‑‑ break grad‐flow
                tgt = target_return.detach()
                pred = state_value.detach()
                eps = torch.finfo(tgt.dtype).eps

                resid = torch.var(tgt - pred, correction=0, dim=0)
                total = torch.var(tgt, correction=0, dim=0)
                explained_variance = 1.0 - resid / (total + eps)

        self._clear_weakrefs(
            tensordict,
            "actor_network_params",
            "critic_network_params",
            "target_actor_network_params",
            "target_critic_network_params",
        )
        if self._has_critic:
            return self.critic_coeff * loss_value, clip_fraction, explained_variance
        return loss_value, clip_fraction, explained_variance

    @property
    @_cache_values
    def _cached_critic_network_params_detached(self):
        if not self.functional:
            return None
        return self.critic_network_params.detach()

    @dispatch
    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
        tensordict = tensordict.clone(False)
        advantage = tensordict.get(self.tensor_keys.advantage, None)
        if advantage is None:
            self.value_estimator(
                tensordict,
                params=self._cached_critic_network_params_detached,
                target_params=self.target_critic_network_params,
            )
            advantage = tensordict.get(self.tensor_keys.advantage)
        if self.normalize_advantage and advantage.numel() > 1:
            if advantage.numel() > tensordict.batch_size.numel() and not len(
                self.normalize_advantage_exclude_dims
            ):
                warnings.warn(
                    "You requested advantage normalization and the advantage key has more dimensions"
                    " than the tensordict batch. Make sure to pass `normalize_advantage_exclude_dims` "
                    "if you want to keep any dimension independent while computing normalization statistics. "
                    "If you are working in multi-agent/multi-objective settings this is highly suggested."
                )
            advantage = _standardize(advantage, self.normalize_advantage_exclude_dims)

        log_weight, dist, kl_approx = self._log_weight(
            tensordict, adv_shape=advantage.shape[:-1]
        )
        neg_loss = log_weight.exp() * advantage
        td_out = TensorDict({"loss_objective": -neg_loss})
        td_out.set("kl_approx", kl_approx.detach().mean())  # for logging
        if self.entropy_bonus:
            entropy = self._get_entropy(dist, adv_shape=advantage.shape[:-1])
            if is_tensor_collection(entropy):
                # Reports the entropy of each action head.
                td_out.set("composite_entropy", entropy.detach())
                td_out.set(
                    "entropy", _sum_td_features(entropy).detach().mean()
                )  # for logging
            else:
                td_out.set("entropy", entropy.detach().mean())  # for logging
            td_out.set("loss_entropy", self._weighted_loss_entropy(entropy))
        if self._has_critic:
            loss_critic, value_clip_fraction, explained_variance = self.loss_critic(
                tensordict
            )
            td_out.set("loss_critic", loss_critic)
            if value_clip_fraction is not None:
                td_out.set("value_clip_fraction", value_clip_fraction)
            if explained_variance is not None:
                td_out.set("explained_variance", explained_variance)
        td_out = td_out.named_apply(
            lambda name, value: _reduce(value, reduction=self.reduction).squeeze(-1)
            if name.startswith("loss_")
            else value,
        )
        self._clear_weakrefs(
            tensordict,
            td_out,
            "actor_network_params",
            "critic_network_params",
            "target_actor_network_params",
            "target_critic_network_params",
        )
        return td_out

    def make_value_estimator(self, value_type: ValueEstimators = None, **hyperparams):
        if value_type is None:
            value_type = self.default_value_estimator
        self.value_type = value_type
        hp = dict(default_value_kwargs(value_type))
        if hasattr(self, "gamma"):
            hp["gamma"] = self.gamma
        hp.update(hyperparams)
        if value_type == ValueEstimators.TD1:
            self._value_estimator = TD1Estimator(
                value_network=self.critic_network, **hp
            )
        elif value_type == ValueEstimators.TD0:
            self._value_estimator = TD0Estimator(
                value_network=self.critic_network, **hp
            )
        elif value_type == ValueEstimators.GAE:
            self._value_estimator = GAE(value_network=self.critic_network, **hp)
        elif value_type == ValueEstimators.TDLambda:
            self._value_estimator = TDLambdaEstimator(
                value_network=self.critic_network, **hp
            )
        elif value_type == ValueEstimators.VTrace:
            # VTrace currently does not support functional call on the actor
            if self.functional:
                actor_with_params = deepcopy(self.actor_network)
                self.actor_network_params.to_module(actor_with_params)
            else:
                actor_with_params = self.actor_network
            self._value_estimator = VTrace(
                value_network=self.critic_network, actor_network=actor_with_params, **hp
            )
        else:
            raise NotImplementedError(f"Unknown value type {value_type}")

        tensor_keys = {
            "advantage": self.tensor_keys.advantage,
            "value": self.tensor_keys.value,
            "value_target": self.tensor_keys.value_target,
            "reward": self.tensor_keys.reward,
            "done": self.tensor_keys.done,
            "terminated": self.tensor_keys.terminated,
            "sample_log_prob": self.tensor_keys.sample_log_prob,
        }
        self._value_estimator.set_keys(**tensor_keys)

    def _weighted_loss_entropy(
        self, entropy: torch.Tensor | TensorDictBase
    ) -> torch.Tensor:
        """Compute the weighted entropy loss.

        If `self._entropy_coeff_map` is provided, apply per-head entropy coefficients.
        Otherwise, use the scalar `self.entropy_coeff`.
        The entries in self._entropy_coeff_map require the full nested key to the entropy head.
        """
        # Mode 1: Use scalar entropy coefficient (default behavior)
        if self._entropy_coeff_map is None:
            # If entropy is a TensorDict (composite action space), sum all entropy values
            if is_tensor_collection(entropy):
                entropy = _sum_td_features(entropy)
            # Apply scalar coefficient: loss = -coeff * entropy (negative for maximization)
            return -self.entropy_coeff * entropy

        # Mode 2: Use per-head entropy coefficients (for composite action spaces)
        loss_term = None  # Initialize running sum over action heads
        coeff = 0  # Placeholder for coefficient value
        # Iterate through all entropy heads in the composite action space
        for head_name, entropy_head in entropy.items(
            include_nested=True, leaves_only=True
        ):
            try:
                # Look up the coefficient for this specific action head
                coeff = self._entropy_coeff_map[head_name]
            except KeyError as exc:
                # Provide clear error message if coefficient mapping is incomplete
                raise KeyError(f"Missing entropy coeff for head '{head_name}'") from exc
            # Convert coefficient to tensor with matching dtype and device
            coeff_t = torch.as_tensor(
                coeff, dtype=entropy_head.dtype, device=entropy_head.device
            )
            # Compute weighted loss for this head: -coeff * entropy
            head_loss_term = -coeff_t * entropy_head
            # Accumulate loss terms across all heads
            loss_term = (
                head_loss_term if loss_term is None else loss_term + head_loss_term
            )

        return loss_term


class ClipPPOLoss(PPOLoss):
    """Clipped PPO loss.

    The clipped importance weighted loss is computed as follows:
        loss = -min( weight * advantage, min(max(weight, 1-eps), 1+eps) * advantage)

    Args:
        actor_network (ProbabilisticTensorDictSequential): policy operator.
        critic_network (ValueOperator): value operator.

    .. note::
        While this loss module does not enforce any specific model mode (train/eval), it is highly recommended
        to keep your model in eval mode during RL training to ensure deterministic behavior.
        A failure to learn due to a train/eval mode mismatch is often observed when the Effective Sample Size (ESS)
        drops or increases significantly (see note below).

    .. note::
        The PPO loss exposes a couple of additional metrics that can be used to monitor the training process:

        - The clip fraction is the ratio of the number of clipped weights in the PPO loss (i.e. the ratio of the number of weights that were clipped to the total number of weights).
        - The Effective Sample Size (ESS) is a measure of the effective number of samples in the batch, computed as the inverse of the sum of the squared importance weights.
          A value of 1 indicates that the importance weights are all equal to 1 (i.e., the samples are equally weighted).
          Any value below 1 indicates that the samples are not equally weighted, and the ESS is a measure of the effective number of samples.
          If the value drops or increases significantly, it often indicates issues with the model configuration (such as a train/eval mode mismatch, or a large policy update).

    Keyword Args:
        clip_epsilon (scalar, optional): weight clipping threshold in the clipped PPO loss equation.
            default: 0.2
        entropy_bonus (bool, optional): if ``True``, an entropy bonus will be added to the
            loss to favour exploratory policies.
        samples_mc_entropy (int, optional): if the distribution retrieved from the policy
            operator does not have a closed form
            formula for the entropy, a Monte-Carlo estimate will be used.
            ``samples_mc_entropy`` will control how many
            samples will be used to compute this estimate.
            Defaults to ``1``.
        entropy_coeff: (scalar | Mapping[NestedKey, scalar], optional): entropy multiplier when computing the total loss.
            * **Scalar**: one value applied to the summed entropy of every action head.
            * **Mapping** ``{head_name: coeff}`` gives an individual coefficient for each action-head's entropy.
            Defaults to ``0.01``.

            See :ref:`ppo_entropy_coefficients` for detailed usage examples and troubleshooting.
        critic_coeff (scalar, optional): critic loss multiplier when computing the total
            loss. Defaults to ``1.0``. Set ``critic_coeff`` to ``None`` to exclude the value
            loss from the forward outputs.
        loss_critic_type (str, optional): loss function for the value discrepancy.
            Can be one of "l1", "l2" or "smooth_l1". Defaults to ``"smooth_l1"``.
        normalize_advantage (bool, optional): if ``True``, the advantage will be normalized
            before being used. Defaults to ``False``.
        normalize_advantage_exclude_dims (Tuple[int], optional): dimensions to exclude from the advantage
            standardization. Negative dimensions are valid. This is useful in multiagent (or multiobjective) settings
            where the agent (or objective) dimension may be excluded from the reductions. Default: ().
        separate_losses (bool, optional): if ``True``, shared parameters between
            policy and critic will only be trained on the policy loss.
            Defaults to ``False``, i.e., gradients are propagated to shared
            parameters for both policy and critic losses.
        advantage_key (str, optional): [Deprecated, use set_keys(advantage_key=advantage_key) instead]
            The input tensordict key where the advantage is
            expected to be written. Defaults to ``"advantage"``.
        value_target_key (str, optional): [Deprecated, use set_keys(value_target_key=value_target_key) instead]
            The input tensordict key where the target state
            value is expected to be written. Defaults to ``"value_target"``.
        value_key (str, optional): [Deprecated, use set_keys(value_key) instead]
            The input tensordict key where the state
            value is expected to be written. Defaults to ``"state_value"``.
        functional (bool, optional): whether modules should be functionalized.
            Functionalizing permits features like meta-RL, but makes it
            impossible to use distributed models (DDP, FSDP, ...) and comes
            with a little cost. Defaults to ``True``.
        reduction (str, optional): Specifies the reduction to apply to the output:
            ``"none"`` | ``"mean"`` | ``"sum"``. ``"none"``: no reduction will be applied,
            ``"mean"``: the sum of the output will be divided by the number of
            elements in the output, ``"sum"``: the output will be summed. Default: ``"mean"``.
        clip_value (bool or float, optional): If a ``float`` is provided, it will be used to compute a clipped
            version of the value prediction with respect to the input tensordict value estimate and use it to
            calculate the value loss. The purpose of clipping is to limit the impact of extreme value predictions,
            helping stabilize training and preventing large updates. However, it will have no impact if the value
            estimate was done by the current version of the value estimator. If instead ``True`` is provided, the
            ``clip_epsilon`` parameter will be used as the clipping threshold. If not provided or ``False``, no
            clipping will be performed. Defaults to ``False``.
        device (torch.device, optional): device of the buffers. Defaults to ``None``.

            .. note:: Parameters and buffers from the policy / critic will not be cast to that device to ensure that
                the storages match the ones that are passed to other components, such as data collectors.

    .. note:
      The advantage (typically GAE) can be computed by the loss function or
      in the training loop. The latter option is usually preferred, but this is
      up to the user to choose which option is to be preferred.
      If the advantage key (``"advantage`` by default) is not present in the
      input tensordict, the advantage will be computed by the :meth:`~.forward`
      method.

        >>> ppo_loss = ClipPPOLoss(actor, critic)
        >>> advantage = GAE(critic)
        >>> data = next(datacollector)
        >>> losses = ppo_loss(data)
        >>> # equivalent
        >>> advantage(data)
        >>> losses = ppo_loss(data)

      A custom advantage module can be built using :meth:`~.make_value_estimator`.
      The default is :class:`~torchrl.objectives.value.GAE` with hyperparameters
      dictated by :func:`~torchrl.objectives.utils.default_value_kwargs`.

        >>> ppo_loss = ClipPPOLoss(actor, critic)
        >>> ppo_loss.make_value_estimator(ValueEstimators.TDLambda)
        >>> data = next(datacollector)
        >>> losses = ppo_loss(data)

    .. note::
      If the actor and the value function share parameters, one can avoid
      calling the common module multiple times by passing only the head of the
      value network to the PPO loss module:

        >>> common = SomeModule(in_keys=["observation"], out_keys=["hidden"])
        >>> actor_head = SomeActor(in_keys=["hidden"])
        >>> value_head = SomeValue(in_keys=["hidden"])
        >>> # first option, with 2 calls on the common module
        >>> model = ActorValueOperator(common, actor_head, value_head)
        >>> loss_module = ClipPPOLoss(model.get_policy_operator(), model.get_value_operator())
        >>> # second option, with a single call to the common module
        >>> loss_module = ClipPPOLoss(ProbabilisticTensorDictSequential(model, actor_head), value_head)

      This will work regardless of whether separate_losses is activated or not.

    """

    actor_network: TensorDictModule
    critic_network: TensorDictModule
    actor_network_params: TensorDictParams
    critic_network_params: TensorDictParams
    target_actor_network_params: TensorDictParams
    target_critic_network_params: TensorDictParams

    def __init__(
        self,
        actor_network: ProbabilisticTensorDictSequential | None = None,
        critic_network: TensorDictModule | None = None,
        *,
        clip_epsilon: float = 0.2,
        entropy_bonus: bool = True,
        samples_mc_entropy: int = 1,
        entropy_coeff: float | Mapping[NestedKey, float] | None = None,
        critic_coeff: float | None = None,
        loss_critic_type: str = "smooth_l1",
        normalize_advantage: bool = False,
        normalize_advantage_exclude_dims: tuple[int] = (),
        gamma: float | None = None,
        separate_losses: bool = False,
        reduction: str | None = None,
        clip_value: bool | float | None = None,
        device: torch.device | None = None,
        **kwargs,
    ):
        # Define clipping of the value loss
        if isinstance(clip_value, bool):
            clip_value = clip_epsilon if clip_value else None

        super().__init__(
            actor_network,
            critic_network,
            entropy_bonus=entropy_bonus,
            samples_mc_entropy=samples_mc_entropy,
            entropy_coeff=entropy_coeff,
            critic_coeff=critic_coeff,
            loss_critic_type=loss_critic_type,
            normalize_advantage=normalize_advantage,
            normalize_advantage_exclude_dims=normalize_advantage_exclude_dims,
            gamma=gamma,
            separate_losses=separate_losses,
            reduction=reduction,
            clip_value=clip_value,
            device=device,
            **kwargs,
        )
        if device is None:
            try:
                device = next(self.parameters()).device
            except (AttributeError, StopIteration):
                device = getattr(
                    torch, "get_default_device", lambda: torch.device("cpu")
                )()
        self.register_buffer("clip_epsilon", torch.tensor(clip_epsilon, device=device))

    @property
    def _clip_bounds(self):
        return (
            (-self.clip_epsilon).log1p(),
            self.clip_epsilon.log1p(),
        )

    @property
    def out_keys(self):
        if self._out_keys is None:
            keys = ["loss_objective", "clip_fraction"]
            if self.entropy_bonus:
                keys.extend(["entropy", "loss_entropy"])
            if self.loss_critic:
                keys.append("loss_critic")
            if self.clip_value:
                keys.append("value_clip_fraction")
            keys.append("ESS")
            self._out_keys = keys
        return self._out_keys

    @out_keys.setter
    def out_keys(self, values):
        self._out_keys = values

    @dispatch
    def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
        tensordict = tensordict.clone(False)
        advantage = tensordict.get(
            self.tensor_keys.advantage, None, as_padded_tensor=True
        )
        if advantage is None:
            if self.critic_network is None:
                raise RuntimeError(
                    "Critic network is not specified, cannot compute advantage within forward."
                )
            self.value_estimator(
                tensordict,
                params=self._cached_critic_network_params_detached,
                target_params=self.target_critic_network_params,
            )
            advantage = tensordict.get(self.tensor_keys.advantage)
        if self.normalize_advantage and advantage.numel() > 1:
            if advantage.numel() > tensordict.batch_size.numel() and not len(
                self.normalize_advantage_exclude_dims
            ):
                warnings.warn(
                    "You requested advantage normalization and the advantage key has more dimensions"
                    " than the tensordict batch. Make sure to pass `normalize_advantage_exclude_dims` "
                    "if you want to keep any dimension independent while computing normalization statistics. "
                    "If you are working in multi-agent/multi-objective settings this is highly suggested."
                )
            advantage = _standardize(advantage, self.normalize_advantage_exclude_dims)

        log_weight, dist, kl_approx = self._log_weight(
            tensordict, adv_shape=advantage.shape[:-1]
        )
        # ESS for logging
        with torch.no_grad():
            # In theory, ESS should be computed on particles sampled from the same source. Here we sample according
            # to different, unrelated trajectories, which is not standard. Still, it can give an idea of the weights'
            # dispersion.
            lw = log_weight.squeeze()
            ess = (2 * lw.logsumexp(0) - (2 * lw).logsumexp(0)).exp()
            batch = log_weight.shape[0]

        gain1 = log_weight.exp() * advantage

        log_weight_clip = log_weight.clamp(*self._clip_bounds)
        clip_fraction = (log_weight_clip != log_weight).to(log_weight.dtype).mean()
        ratio = log_weight_clip.exp()
        gain2 = ratio * advantage

        gain = torch.stack([gain1, gain2], -1).min(dim=-1).values
        td_out = TensorDict({"loss_objective": -gain})
        td_out.set("clip_fraction", clip_fraction)
        td_out.set("kl_approx", kl_approx.detach().mean())  # for logging

        if self.entropy_bonus:
            entropy = self._get_entropy(dist, adv_shape=advantage.shape[:-1])
            if is_tensor_collection(entropy):
                # Reports the entropy of each action head.
                td_out.set("composite_entropy", entropy.detach())
                td_out.set(
                    "entropy", _sum_td_features(entropy).detach().mean()
                )  # for logging
            else:
                td_out.set("entropy", entropy.detach().mean())  # for logging
            td_out.set("loss_entropy", self._weighted_loss_entropy(entropy))
        if self._has_critic:
            loss_critic, value_clip_fraction, explained_variance = self.loss_critic(
                tensordict
            )
            td_out.set("loss_critic", loss_critic)
            if value_clip_fraction is not None:
                td_out.set("value_clip_fraction", value_clip_fraction)
            if explained_variance is not None:
                td_out.set("explained_variance", explained_variance)

        td_out.set("ESS", _reduce(ess, self.reduction) / batch)
        td_out = td_out.named_apply(
            lambda name, value: _reduce(value, reduction=self.reduction).squeeze(-1)
            if name.startswith("loss_")
            else value,
        )
        self._clear_weakrefs(
            tensordict,
            td_out,
            "actor_network_params",
            "critic_network_params",
            "target_actor_network_params",
            "target_critic_network_params",
        )
        return td_out


class KLPENPPOLoss(PPOLoss):
    """KL Penalty PPO loss.

    The KL penalty loss has the following formula:
        loss = loss - beta * KL(old_policy, new_policy)
    The "beta" parameter is adapted on-the-fly to match a target KL divergence between the new and old policy, thus
    favouring a certain level of distancing between the two while still preventing them to be too much apart.

    Args:
        actor_network (ProbabilisticTensorDictSequential): policy operator.
        critic_network (ValueOperator): value operator.

    Keyword Args:
        dtarg (scalar, optional): target KL divergence. Defaults to ``0.01``.
        samples_mc_kl (int, optional): number of samples used to compute the KL divergence
            if no analytical formula can be found. Defaults to ``1``.
        beta (scalar, optional): initial KL divergence multiplier.
            Defaults to ``1.0``.
        decrement (scalar, optional): how much beta should be decremented if KL < dtarg. Valid range: decrement <= 1.0
            default: ``0.5``.
        increment (scalar, optional): how much beta should be incremented if KL > dtarg. Valid range: increment >= 1.0
            default: ``2.0``.
        entropy_bonus (bool, optional): if ``True``, an entropy bonus will be added to the
            loss to favour exploratory policies. Defaults to ``True``.
        samples_mc_entropy (int, optional): if the distribution retrieved from the policy
            operator does not have a closed form
            formula for the entropy, a Monte-Carlo estimate will be used.
            ``samples_mc_entropy`` will control how many
            samples will be used to compute this estimate.
            Defaults to ``1``.
        entropy_coeff: scalar | Mapping[NestedKey, scalar], optional): entropy multiplier when computing the total loss.
            * **Scalar**: one value applied to the summed entropy of every action head.
            * **Mapping** ``{head_name: coeff}`` gives an individual coefficient for each action-head's entropy.
            Defaults to ``0.01``.

            See :ref:`ppo_entropy_coefficients` for detailed usage examples and troubleshooting.
        critic_coeff (scalar, optional): critic loss multiplier when computing the total
            loss. Defaults to ``1.0``.
        loss_critic_type (str, optional): loss function for the value discrepancy.
            Can be one of "l1", "l2" or "smooth_l1". Defaults to ``"smooth_l1"``.
        normalize_advantage (bool, optional): if ``True``, the advantage will be normalized
            before being used. Defaults to ``False``.
        normalize_advantage_exclude_dims (Tuple[int], optional): dimensions to exclude from the advantage
            standardization. Negative dimensions are valid. This is useful in multiagent (or multiobjective) settings
            where the agent (or objective) dimension may be excluded from the reductions. Default: ().
        separate_losses (bool, optional): if ``True``, shared parameters between
            policy and critic will only be trained on the policy loss.
            Defaults to ``False``, i.e., gradients are propagated to shared
            parameters for both policy and critic losses.
        advantage_key (str, optional): [Deprecated, use set_keys(advantage_key=advantage_key) instead]
            The input tensordict key where the advantage is
            expected to be written. Defaults to ``"advantage"``.
        value_target_key (str, optional): [Deprecated, use set_keys(value_target_key=value_target_key) instead]
            The input tensordict key where the target state
            value is expected to be written. Defaults to ``"value_target"``.
        value_key (str, optional): [Deprecated, use set_keys(value_key) instead]
            The input tensordict key where the state
            value is expected to be written. Defaults to ``"state_value"``.
        functional (bool, optional): whether modules should be functionalized.
            Functionalizing permits features like meta-RL, but makes it
            impossible to use distributed models (DDP, FSDP, ...) and comes
            with a little cost. Defaults to ``True``.
        reduction (str, optional): Specifies the reduction to apply to the output:
            ``"none"`` | ``"mean"`` | ``"sum"``. ``"none"``: no reduction will be applied,
            ``"mean"``: the sum of the output will be divided by the number of
            elements in the output, ``"sum"``: the output will be summed. Default: ``"mean"``.
        clip_value (:obj:`float`, optional): If provided, it will be used to compute a clipped version of the value
            prediction with respect to the input tensordict value estimate and use it to calculate the value loss.
            The purpose of clipping is to limit the impact of extreme value predictions, helping stabilize training
            and preventing large updates. However, it will have no impact if the value estimate was done by the current
            version of the value estimator. Defaults to ``None``.
        device (torch.device, optional): device of the buffers. Defaults to ``None``.

            .. note:: Parameters and buffers from the policy / critic will not be cast to that device to ensure that
                the storages match the ones that are passed to other components, such as data collectors.

    .. note:
      The advantage (typically GAE) can be computed by the loss function or
      in the training loop. The latter option is usually preferred, but this is
      up to the user to choose which option is to be preferred.
      If the advantage key (``"advantage`` by default) is not present in the
      input tensordict, the advantage will be computed by the :meth:`~.forward`
      method.

        >>> ppo_loss = KLPENPPOLoss(actor, critic)
        >>> advantage = GAE(critic)
        >>> data = next(datacollector)
        >>> losses = ppo_loss(data)
        >>> # equivalent
        >>> advantage(data)
        >>> losses = ppo_loss(data)

      A custom advantage module can be built using :meth:`~.make_value_estimator`.
      The default is :class:`~torchrl.objectives.value.GAE` with hyperparameters
      dictated by :func:`~torchrl.objectives.utils.default_value_kwargs`.

        >>> ppo_loss = KLPENPPOLoss(actor, critic)
        >>> ppo_loss.make_value_estimator(ValueEstimators.TDLambda)
        >>> data = next(datacollector)
        >>> losses = ppo_loss(data)

    .. note::
      If the actor and the value function share parameters, one can avoid
      calling the common module multiple times by passing only the head of the
      value network to the PPO loss module:

        >>> common = SomeModule(in_keys=["observation"], out_keys=["hidden"])
        >>> actor_head = SomeActor(in_keys=["hidden"])
        >>> value_head = SomeValue(in_keys=["hidden"])
        >>> # first option, with 2 calls on the common module
        >>> model = ActorValueOperator(common, actor_head, value_head)
        >>> loss_module = KLPENPPOLoss(model.get_policy_operator(), model.get_value_operator())
        >>> # second option, with a single call to the common module
        >>> loss_module = KLPENPPOLoss(ProbabilisticTensorDictSequential(model, actor_head), value_head)

      This will work regardless of whether separate_losses is activated or not.

    """

    actor_network: TensorDictModule
    critic_network: TensorDictModule
    actor_network_params: TensorDictParams
    critic_network_params: TensorDictParams
    target_actor_network_params: TensorDictParams
    target_critic_network_params: TensorDictParams

    def __init__(
        self,
        actor_network: ProbabilisticTensorDictSequential | None = None,
        critic_network: TensorDictModule | None = None,
        *,
        dtarg: float = 0.01,
        beta: float = 1.0,
        increment: float = 2,
        decrement: float = 0.5,
        samples_mc_kl: int = 1,
        entropy_bonus: bool = True,
        samples_mc_entropy: int = 1,
        entropy_coeff: float | Mapping[NestedKey, float] | None = None,
        critic_coeff: float | None = None,
        loss_critic_type: str = "smooth_l1",
        normalize_advantage: bool = False,
        normalize_advantage_exclude_dims: tuple[int] = (),
        gamma: float | None = None,
        separate_losses: bool = False,
        reduction: str | None = None,
        clip_value: float | None = None,
        device: torch.device | None = None,
        **kwargs,
    ):
        super().__init__(
            actor_network,
            critic_network,
            entropy_bonus=entropy_bonus,
            samples_mc_entropy=samples_mc_entropy,
            entropy_coeff=entropy_coeff,
            critic_coeff=critic_coeff,
            loss_critic_type=loss_critic_type,
            normalize_advantage=normalize_advantage,
            normalize_advantage_exclude_dims=normalize_advantage_exclude_dims,
            gamma=gamma,
            separate_losses=separate_losses,
            reduction=reduction,
            clip_value=clip_value,
            device=device,
            **kwargs,
        )

        if device is None:
            try:
                device = next(self.parameters()).device
            except (AttributeError, StopIteration):
                device = getattr(
                    torch, "get_default_device", lambda: torch.device("cpu")
                )()

        self.dtarg = dtarg
        self._beta_init = beta
        self.register_buffer("beta", torch.tensor(beta, device=device))

        if increment < 1.0:
            raise ValueError(
                f"increment should be >= 1.0 in KLPENPPOLoss, got {increment:4.4f}"
            )
        self.increment = increment
        if decrement > 1.0:
            raise ValueError(
                f"decrement should be <= 1.0 in KLPENPPOLoss, got {decrement:4.4f}"
            )
        self.decrement = decrement
        self.samples_mc_kl = samples_mc_kl

    def _set_in_keys(self):
        keys = []
        _maybe_add_or_extend_key(keys, self.actor_network.in_keys)
        _maybe_add_or_extend_key(keys, self.actor_network.in_keys, "next")
        if self.critic_network is not None:
            _maybe_add_or_extend_key(keys, self.critic_network.in_keys)
        _maybe_add_or_extend_key(keys, self.tensor_keys.action)
        _maybe_add_or_extend_key(keys, self.tensor_keys.sample_log_prob)
        _maybe_add_or_extend_key(keys, self.tensor_keys.reward, "next")
        _maybe_add_or_extend_key(keys, self.tensor_keys.done, "next")
        _maybe_add_or_extend_key(keys, self.tensor_keys.terminated, "next")

        # Get the parameter keys from the actor dist
        actor_dist_module = None
        for module in self.actor_network.modules():
            # Ideally we should combine them if there is more than one
            if isinstance(module, ProbabilisticTensorDictModule):
                if actor_dist_module is not None:
                    raise RuntimeError(
                        "Actors with one and only one distribution are currently supported "
                        f"in {type(self).__name__}. If you need to use more than one "
                        f"distributions over the action space please submit an issue "
                        f"on github."
                    )
                actor_dist_module = module
        if actor_dist_module is None:
            raise RuntimeError("Could not find the probabilistic module in the actor.")
        keys += list(actor_dist_module.in_keys)
        self._in_keys = list(set(keys))

    @property
    def out_keys(self):
        if self._out_keys is None:
            keys = ["loss_objective", "kl"]
            if self.entropy_bonus:
                keys.extend(["entropy", "loss_entropy"])
            if self.loss_critic:
                keys.append("loss_critic")
            if self.clip_value:
                keys.append("value_clip_fraction")
            self._out_keys = keys
        return self._out_keys

    @out_keys.setter
    def out_keys(self, values):
        self._out_keys = values

    @dispatch
    def forward(self, tensordict: TensorDictBase) -> TensorDict:
        tensordict_copy = tensordict.copy()
        try:
            previous_dist = self.actor_network.build_dist_from_params(tensordict)
        except KeyError as err:
            raise KeyError(
                "The parameters of the distribution were not found. "
                f"Make sure they are provided to {type(self).__name__}."
            ) from err
        advantage = tensordict_copy.get(self.tensor_keys.advantage, None)
        if advantage is None:
            self.value_estimator(
                tensordict_copy,
                params=self._cached_critic_network_params_detached,
                target_params=self.target_critic_network_params,
            )
            advantage = tensordict_copy.get(self.tensor_keys.advantage)
        if self.normalize_advantage and advantage.numel() > 1:
            if advantage.numel() > tensordict.batch_size.numel() and not len(
                self.normalize_advantage_exclude_dims
            ):
                warnings.warn(
                    "You requested advantage normalization and the advantage key has more dimensions"
                    " than the tensordict batch. Make sure to pass `normalize_advantage_exclude_dims` "
                    "if you want to keep any dimension independent while computing normalization statistics. "
                    "If you are working in multi-agent/multi-objective settings this is highly suggested."
                )
            advantage = _standardize(advantage, self.normalize_advantage_exclude_dims)

        log_weight, dist, kl_approx = self._log_weight(
            tensordict_copy, adv_shape=advantage.shape[:-1]
        )
        neg_loss = log_weight.exp() * advantage

        with self.actor_network_params.to_module(
            self.actor_network
        ) if self.functional else contextlib.nullcontext():
            current_dist = self.actor_network.get_dist(tensordict_copy)
        is_composite = isinstance(current_dist, CompositeDistribution)
        try:
            kl = torch.distributions.kl.kl_divergence(previous_dist, current_dist)
        except NotImplementedError:
            x = previous_dist.sample((self.samples_mc_kl,))
            with set_composite_lp_aggregate(
                False
            ) if is_composite else contextlib.nullcontext():
                previous_log_prob = previous_dist.log_prob(x)
                current_log_prob = current_dist.log_prob(x)
            if is_tensor_collection(previous_log_prob):
                if previous_log_prob.batch_size != advantage.shape[:-1]:
                    previous_log_prob.batch_size = (
                        self.samples_mc_kl,
                    ) + advantage.shape[:-1]
                    current_log_prob.batch_size = (
                        self.samples_mc_kl,
                    ) + advantage.shape[:-1]
                previous_log_prob = _sum_td_features(previous_log_prob)
                # Both dists have presumably the same params
                current_log_prob = _sum_td_features(current_log_prob)
            kl = (previous_log_prob - current_log_prob).mean(0)
        kl = kl.unsqueeze(-1)
        neg_loss = neg_loss - self.beta * kl
        if kl.mean() > self.dtarg * 1.5:
            self.beta.data *= self.increment
        elif kl.mean() < self.dtarg / 1.5:
            self.beta.data *= self.decrement
        td_out = TensorDict(
            {
                "loss_objective": -neg_loss,
                "kl": kl.detach(),
                "kl_approx": kl_approx.detach().mean(),
            },
        )

        if self.entropy_bonus:
            entropy = self._get_entropy(dist, adv_shape=advantage.shape[:-1])
            if is_tensor_collection(entropy):
                # Reports the entropy of each action head.
                td_out.set("composite_entropy", entropy.detach())
                td_out.set(
                    "entropy", _sum_td_features(entropy).detach().mean()
                )  # for logging
            else:
                td_out.set("entropy", entropy.detach().mean())  # for logging
            td_out.set("loss_entropy", self._weighted_loss_entropy(entropy))
        if self._has_critic:
            loss_critic, value_clip_fraction, explained_variance = self.loss_critic(
                tensordict_copy
            )
            td_out.set("loss_critic", loss_critic)
            if value_clip_fraction is not None:
                td_out.set("value_clip_fraction", value_clip_fraction)
            if explained_variance is not None:
                td_out.set("explained_variance", explained_variance)
        td_out = td_out.named_apply(
            lambda name, value: _reduce(value, reduction=self.reduction).squeeze(-1)
            if name.startswith("loss_")
            else value,
        )
        self._clear_weakrefs(
            tensordict,
            td_out,
            "actor_network_params",
            "critic_network_params",
            "target_actor_network_params",
            "target_critic_network_params",
        )
        return td_out

    def reset(self) -> None:
        self.beta = self._beta_init
